Predicting Hemodynamic Shock from Thermal Images using Machine Learning

Proactive detection of hemodynamic shock can prevent organ failure and save lives. Thermal imaging is a non-invasive, non-contact modality to capture body surface temperature with the potential to reveal underlying perfusion disturbance in shock. In this study, we automate early detection and predic...

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Detalles Bibliográficos
Autores principales: Nagori, Aditya, Dhingra, Lovedeep Singh, Bhatnagar, Ambika, Lodha, Rakesh, Sethi, Tavpritesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331545/
https://www.ncbi.nlm.nih.gov/pubmed/30643187
http://dx.doi.org/10.1038/s41598-018-36586-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331545/
https://www.ncbi.nlm.nih.gov/pubmed/30643187
http://dx.doi.org/10.1038/s41598-018-36586-8

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